Discussion
1. What could you have done better in your experiment design and setup?
The servo in this experiment is held in position through a 7lb weight and a vice. I could have
3d printed a mount for the servo that was screwed on a piece of wood. The pipe could have
been replaced by a more practical bowl or plate rather than a rusted piece of metal. The rope
could have been cut down to size so there wouldn’t be so many loops to hang from. A protractor
could have been used to measure the lever angle. An encoder could have been used to get a
digital reading of the servo pinion to verify.
2. Discuss your rationale for the model you selected. Describe any assumptions or
simplifications this model makes. Include external references used in selecting or
understanding your model.
This model is a great representation of the servo’s holding torque because, by adding weight
to lever, the force on the lever is translated to torque on the servo pinion. Hence, servo tries to
use position control signal from the driver to keep the lever in the horizontal position. To
counter the external torque, the servo pulls more current from the driver to generate more
power and maintain the position of the level. [1]
3. Justify the method you selected (least squares, nonlinear least squares,
scipy.optimize.minimize(), Evolutionary algorithm, etc. ) for fitting experimental data
to the model, as well as the specific algorithm used.
I used to excel to generate the plot for my collected data. As the weight on the lever
increases, the motor needs more power to counter the applied torque, this power is generated
through pulling more current from the source (P=V*I). It can be concluded that there is a
linear relationship between the current usage and the external torque on the motor. As
demonstrated in the least square example [2] for fitting the data of the linear model.
4. How well does your data fit the model you selected? Provide a numerical value as well
as a qualitative analysis, using your figure to explain.
Using the R value generated through excel in figure 13, it can be concluded that the linear
trendline is 95% accurate. When R is 1, that means the data and the model have a 1 to 1
relationship. In this case the value of 0.95 can be accounted as 95%. The linear trendline is
also a good verification since we already know that the current consumption and motor
torque have a linear relationship.
5. What are the limits of your model, within which you are confident of a good fit? Do you
expect your system to operate outside of those limits?
When there is no load applied to the lever, at a stationary position the servo’s current usage is
negligible. Once the weight on the lever surpasses the servo’s stall torque, the level drastically
drops its position since the motor cannot hold the lever stationary at horizontal position. In this